A quantum swarm evolutionary algorithm for mining association rules in large databases

Abstract

Association rule mining aims to extract the correlation or Casual structure existing between a set of frequent items or attributes in a database These associations are represented by mean of rules Association rule mining methods provide a robust but nonlinear approach to find associations The search for association rules is an NPcomplete problem The complexities mainly arise in exploiting huge number of database transactions and items In this article we propose a new algorithm to extract the best rules in a reasonable time or execution but without assuring always the optimaI solutions The new derived algorithm is based on Quantum Swarm Evolutionary approach ; it gives better results compared to genetic algorithms